Computationally modeling individual differences in probabilistic decision-making across positive and negative valence domains

对正价域和负价域概率决策的个体差异进行计算建模

基本信息

批准号：
10241535
负责人：
Sonia Jane Bishop
金额：
$ 55.62万
依托单位：
UNIVERSITY OF CALIFORNIA BERKELEY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-19 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10241535
关键词：
Address Adult Amygdaloid structure Anterior Anxiety Bayesian Analysis Bayesian Modeling Behavior Behavioral Behavioral Model Brain Clinical Research Complement Computer Models Corpus striatum structure Data Decision Making Development Dimensions Dorsal Elements Fright Functional Magnetic Resonance Imaging Future Goals Human Individual Differences Learning Life Link Literature Maps Measures Mental Depression Modeling National Institute of Mental Health Negative Valence Operant Conditioning Outcome Participant Patient Self-Report Physiological Play Positive Valence Prefrontal Cortex Probability Psychological reinforcement Psychopathology Punishment Research Research Domain Criteria Rewards Risk Role Shock Structure System Task Performances Uncertainty Update Work base brain circuitry cingulate cortex computer framework depressive symptoms design improved indexing interest neural circuit outcome prediction relating to nervous system response symptomatology

项目摘要

PROJECT SUMMARY In our daily lives, we choose between different courses of action with the hope of achieving desired positive outcomes and avoiding feared negative outcomes; this is complicated by various forms of uncertainty that impact the probability that any given action will result in a particular outcome. Within NIMH’s RDoC framework, studies of the mechanisms involved in reward-based action valuation and choice have informed constructs listed under the Positive Valence Systems (PVS) domain. Associated paradigms examine how differences in outcome probability (first order uncertainty) and action-outcome contingency uncertainty (second order uncertainty) impact choice between alternate options. Within the Negative Valence Systems (NVS) domain, the construct of potential threat (anxiety) does not include consideration of the impact of potential threat, its probability and action- outcome contingency uncertainty, upon action valuation or choice; in addition paradigms listed under the NVS domain have no choice (instrumental) element and use physiological indices as dependent measures. These differences between constructs and tasks across the PVS and NVS domains hinder attempts to elucidate whether psychopathology-related deficits in probabilistic decision-making and the factors influencing action valuation and choice are common across both domains or unique to one or the other. Here, we will address this by creating equivalent PVs (reward) and NVS (shock) versions of two probabilistic decision-making tasks. We will use a hierarchical Bayesian computational framework to model behavioral and brain (functional magnetic resonance imaging) data from PVS and NVS versions of each task. This data will be acquired from healthy adult humans with a range of anxiety and depressive symptomatology. In addition to group-level analyses, we will use bifactor analysis to examine the latent factors underlying variance in anxiety and depressive symptomatology across participants and will relate scores on these factors to parameter estimates obtained by modeling of behavioral and brain data. Using this approach, we will examine commonalities and differences in the mechanisms supporting probabilistic decision-making when potential outcomes are aversive versus rewarding and alterations to these mechanisms as a function of anxiety and depressive related symptomatology. We hope that this will advance our understanding of the aspects of decision-making disrupted in anxiety and depression and the potential consequences for daily life. An additional goal of this research is to provide tasks and models that can be used in future clinical studies of probabilistic decision-making across both PVS and NVS domains.

项目概要在日常生活中，我们会选择不同的行动方案，希望能达到预期的积极效果各种形式的不确定性影响使情况变得复杂；研究表明，在 NIMH 的 RDoC 框架内，任何特定行动都会导致特定结果的概率。涉及基于奖励的行动评估和选择的机制已为下面列出的结构提供了信息正价系统（PVS）领域相关范式检查结果的差异。概率（一阶不确定性）和行动结果偶然不确定性（二阶不确定性）在负价系统（NVS）领域内，影响替代选项之间的选择。潜在威胁（焦虑）不包括对潜在威胁的影响、其概率和行动的考虑—— 根据行动评估或选择，结果的偶然性不确定性；以及 NVS 列出的范式；域没有选择（工具）元素并使用生理指标作为依赖测量。 PVS 和 NVS 领域的构造和任务之间的差异阻碍了阐明的尝试概率决策中是否存在与精神病理学相关的缺陷以及影响行动的因素估值和选择在两个领域中都是常见的，或者是其中一个领域所独有的。在这里，我们将解决这一问题。通过创建两个概率决策任务的同等 PV（奖励）和 NVS（冲击）版本。将使用分层贝叶斯计算框架来模拟行为和大脑（功能磁来自每个任务的 PVS 和 NVS 版本的共振成像）数据该数据将从健康成人处获取。除了群体层面的分析之外，我们还将使用具有一系列焦虑和抑郁症状的人类。双因素分析，检查焦虑和抑郁症状差异背后的潜在因素跨参与者，并将这些因素的分数与通过建模获得的参数估计联系起来使用这种方法，我们将检查行为和大脑数据的共性和差异。当潜在结果令人厌恶而不是有益时支持概率决策的机制我们希望这些机制的改变可以作为焦虑和抑郁相关症状的函数。这将增进我们对焦虑和抑郁导致的决策中断的理解这项研究的另一个目标是提供任务和模型。可用于未来跨 PVS 和 NVS 领域概率决策的临床研究。